Artificial intelligence: a new field of knowledge for nephrologists?

Artificial intelligence (AI) is a science that involves creating machines that can imitate human intelligence and learn. AI is ubiquitous in our daily lives, from search engines like Google to home assistants like Alexa and, more recently, OpenAI with its chatbot. AI can improve clinical care and research, but its use requires a solid understanding of its fundamentals, the promises and perils of algorithmic fairness, the barriers and solutions to its clinical implementation, and the pathways to developing an AI-competent workforce. The potential of AI in the field of nephrology is vast, particularly in the areas of diagnosis, treatment and prediction. One of the most significant advantages of AI is the ability to improve diagnostic accuracy. Machine learning algorithms can be trained to recognize patterns in patient data, including lab results, imaging and medical history, in order to identify early signs of kidney disease and thereby allow timely diagnoses and prompt initiation of treatment plans that can improve outcomes for patients. In short, AI holds the promise of advancing personalized medicine to new levels. While AI has tremendous potential, there are also significant challenges to its implementation, including data access and quality, data privacy and security, bias, trustworthiness, computing power, AI integration and legal issues. The European Commission's proposed regulatory framework for AI technology will play a significant role in ensuring the safe and ethical implementation of these technologies in the healthcare industry. Training nephrologists in the fundamentals of AI is imperative because traditionally, decision-making pertaining to the diagnosis, prognosis and treatment of renal patients has relied on ingrained practices, whereas AI serves as a powerful tool for swiftly and confidently synthesizing this information.

EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping.

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups-WNT, SHH, and non-WNT/non-SHH-directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application-EpiGe-App-that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.t.

MMP1 drives tumor progression in large cell carcinoma of the lung through fibroblast senescence.

Large cell carcinoma (LCC) is a rare and aggressive lung cancer subtype with poor prognosis and no targeted therapies. Tumor-associated fibroblasts (TAFs) derived from LCC tumors exhibit premature senescence, and coculture of pulmonary fibroblasts with LCC cell lines selectively induces fibroblast senescence, which in turn drives LCC cell growth and invasion. Here we identify MMP1 as overexpressed specifically in LCC cell lines, and we show that expression of MMP1 by LCC cells is necessary for induction of fibroblast senescence and consequent tumor promotion in both cell culture and mouse models. We also show that MMP1, in combination with TGF-ß1, is sufficient to induce fibroblast senescence and consequent LCC promotion. Furthermore, we implicate PAR-1 and oxidative stress in MMP1/TGF-ß1-induced TAF senescence. Our results establish an entirely new role for MMP1 in cancer, and support a novel therapeutic strategy in LCC based on targeting senescent TAFs.

Eye Vergence Responses During an Attention Task in Adults With ADHD and Clinical Controls.

Objective: ADHD patients show poor oculomotor control and recent studies show that attention-related eye vergence is weak in ADHD children. We aimed to assess vergence as a potential diagnostic biomarker for ADHD in adults. Method: We assessed the modulation in the angle of vergence while performing an attention task (N = 144), comparing the results for adults previously diagnosed with ADHD (N = 108) with age-matched clinical controls (N = 36). Results: Significant differences in eye vergence response modulation between clinical controls and ADHD patients were documented. Diagnostic test accuracy was 79%. Conclusion: In combination with an attention task, eye vergence responses could be used as an objective marker to support the clinical diagnosis of adult ADHD.

Effects and Mechanisms of Cognitive, Aerobic Exercise, and Combined Training on Cognition, Health, and Brain Outcomes in Physically Inactive Older Adults: The Projecte Moviment Protocol.

INTRODUCTION: Age-related health, brain, and cognitive impairment is a great challenge in current society. Cognitive training, aerobic exercise and their combination have been shown to benefit health, brain, cognition and psychological status in healthy older adults. Inconsistent results across studies may be related to several variables. We need to better identify cognitive changes, individual variables that may predict the effect of these interventions, and changes in structural and functional brain outcomes as well as physiological molecular correlates that may be mediating these effects. Projecte Moviment is a multi-domain randomized trial examining the effect of these interventions applied 5 days per week for 3 months compared to a passive control group. The aim of this paper is to describe the sample, procedures and planned analyses. METHODS: One hundred and forty healthy physically inactive older adults will be randomly assigned to computerized cognitive training (CCT), aerobic exercise (AE), combined training (COMB), or a control group. The intervention consists of a 3 month home-based program 5 days per week in sessions of 45 min. Data from cognitive, physical, and psychological tests, cardiovascular risk factors, structural and functional brain scans, and blood samples will be obtained before and after the intervention. RESULTS: Effects of the interventions on cognitive outcomes will be described in intention-to-treat and per protocol analyses. We will also analyze potential genetic, demographic, brain, and physiological molecular correlates that may predict the effects of intervention, as well as the association between cognitive effects and changes in these variables using the per protocol sample. DISCUSSION: Projecte Moviment is a multi-domain intervention trial based on prior evidence that aims to understand the effects of CCT, AE, and COMB on cognitive and psychological outcomes compared to a passive control group, and to determine related biological correlates and predictors of the intervention effects.Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT03123900.

Validity of the Polar V800 monitor for measuring heart rate variability in mountain running route conditions.

PURPOSE: This study was conducted to test, in mountain running route conditions, the accuracy of the Polar V800™ monitor as a suitable device for monitoring the heart rate variability (HRV) of runners. METHOD: Eighteen healthy subjects ran a route that included a range of running slopes such as those encountered in trail and ultra-trail races. The comparative study of a V800 and a Holter SEER 12 ECG Recorder™ included the analysis of RR time series and short-term HRV analysis. A correction algorithm was designed to obtain the corrected Polar RR intervals. Six 5-min segments related to different running slopes were considered for each subject. RESULTS: The correlation between corrected V800 RR intervals and Holter RR intervals was very high (r = 0.99, p < 0.001), and the bias was less than 1 ms. The limits of agreement (LoA) obtained for SDNN and RMSSD were (- 0.25 to 0.32 ms) and (- 0.90 to 1.08 ms), respectively. The effect size (ES) obtained in the time domain HRV parameters was considered small (ES < 0.2). Frequency domain HRV parameters did not differ (p > 0.05) and were well correlated (r ≥ 0.96, p < 0.001). CONCLUSION: Narrow limits of agreement, high correlations and small effect size suggest that the Polar V800 is a valid tool for the analysis of heart rate variability in athletes while running high endurance events such as marathon, trail, and ultra-trail races.

Affected pathways and transcriptional regulators in gene expression response to an ultra-marathon trail: Global and independent activity approaches.

Gene expression (GE) analyses on blood samples from marathon and half-marathon runners have reported significant impacts on the immune and inflammatory systems. An ultra-marathon trail (UMT) represents a greater effort due to its more testing conditions. For the first time, we report the genome-wide GE profiling in a group of 16 runners participating in an 82 km UMT competition. We quantified their differential GE profile before and after the race using HuGene2.0st microarrays (Affymetrix Inc., California, US). The results obtained were decomposed by means of an independent component analysis (ICA) targeting independent expression modes. We observed significant differences in the expression levels of 5,084 protein coding genes resulting in an overrepresentation of 14% of the human biological pathways from the Kyoto Encyclopedia of Genes and Genomes database. These were mainly clustered on terms related with protein synthesis repression, altered immune system and infectious diseases related mechanisms. In a second analysis, 27 out of the 196 transcriptional regulators (TRs) included in the Open Regulatory Annotation database were overrepresented. Among these TRs, we identified transcription factors from the hypoxia-inducible factors (HIF) family EPAS1 (p< 0.01) and HIF1A (p<0.001), and others jointly described in the gluconeogenesis program such as HNF4 (p< 0.001), EGR1 (p<0.001), CEBPA (p< 0.001) and a highly specific TR, YY1 (p<0.01). The five independent components, obtained from ICA, further revealed a down-regulation of 10 genes distributed in the complex I, III and V from the electron transport chain. This mitochondrial activity reduction is compatible with HIF-1 system activation. The vascular endothelial growth factor (VEGF) pathway, known to be regulated by HIF, also emerged (p<0.05). Additionally, and related to the brain rewarding circuit, the endocannabinoid signalling pathway was overrepresented (p<0.05).

The Central Role of KNG1 Gene as a Genetic Determinant of Coagulation Pathway-Related Traits: Exploring Metaphenotypes.

Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables (linear combinations of the original phenotypes), named metaphenotypes, by applying Independent Component Analysis for the multivariate analysis of correlated phenotypes (i.e. the levels of coagulation pathway-related proteins). The resulting metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project (Genetic Analysis of Idiopathic Thrombophilia). We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases.

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography-mass spectrometry-based metabolomics signals by multivariate methods.

Gas chromatography-mass spectrometry (GC-MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC-MS can be resolved by taking advantage of the multivariate nature of GC-MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis-orthogonal signal deconvolution (ICA-OSD) and multivariate curve resolution-alternating least squares (MCR-ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC-qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA-OSD and MCR-ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R2 coefficients of determination exhibited a high correlation (R2>0.90) in both ICA-OSD and MCR-ALS moving window-based approaches.

Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics.

Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R(2) coefficients of determination were in good agreement (R(2)>0.90) while up to 24 cases exhibited an excellent linear relation (R(2)>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS.

solarius: an R interface to SOLAR for variance component analysis in pedigrees.

UNLABELLED: : The open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for quantitative genetic analyses are stand-alone free programs developed by researchers in academia. SOLAR is one of the standard software programs to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It parses also the model output and contains summary and plotting functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses that makes the calculation of genome-wide scans more efficient. AVAILABILITY AND IMPLEMENTATION: solarius is available on CRAN and on GitHub https://github.com/ugcd/solarius CONTACT: : aziyatdinov@santpau.cat.

Clinical phenotype clustering in cardiovascular risk patients for the identification of responsive metabotypes after red wine polyphenol intake.

This study aims to evaluate the robustness of clinical and metabolic phenotyping through, for the first time, the identification of differential responsiveness to dietary strategies in the improvement of cardiometabolic risk conditions. Clinical phenotyping of 57 volunteers with cardiovascular risk factors was achieved using k-means cluster analysis based on 69 biochemical and anthropometric parameters. Cluster validation based on Dunn and Figure of Merit analysis for internal coherence and external homogeneity were employed. k-Means produced four clusters with particular clinical profiles. Differences on urine metabolomic profiles among clinical phenotypes were explored and validated by multivariate orthogonal signal correction partial least-squares discriminant analysis (OSC-PLS-DA) models. OSC-PLS-DA of (1)H-NMR data revealed that model comparing "obese and diabetic cluster" (OD-c) against "healthier cluster" (H-c) showed the best predictability and robustness in terms of explaining the pairwise differences between clusters. Considering these two clusters, distinct groups of metabolites were observed following an intervention with wine polyphenol intake (WPI; 733 equivalents of gallic acid/day) per 28days. Glucose was significantly linked to OD-c metabotype (P<.01), and lactate, betaine and dimethylamine showed a significant trend. Tartrate (P<.001) was associated with wine polyphenol intervention (OD-c_WPI and H-c_WPI), whereas mannitol, threonine methanol, fucose and 3-hydroxyphenylacetate showed a significant trend. Interestingly, 4-hydroxyphenylacetate significantly increased in H-c_WPI compared to OD-c_WPI and to basal groups (P<.05)-gut microbial-derived metabolite after polyphenol intake-, thereby exhibiting a clear metabotypic intervention effect. Results revealed gut microbiota responsive phenotypes to wine polyphenols intervention. Overall, this study illustrates a novel metabolomic strategy for characterizing interindividual responsiveness to dietary intervention and identification of health benefits.

Sequence information gain based motif analysis.

BACKGROUND: The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions. RESULTS: This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70% of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions. CONCLUSIONS: Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at http://b2slab.upc.edu.

Aberrant DNA methylation in non-small cell lung cancer-associated fibroblasts.

Epigenetic changes through altered DNA methylation have been implicated in critical aspects of tumor progression, and have been extensively studied in a variety of cancer types. In contrast, our current knowledge of the aberrant genomic DNA methylation in tumor-associated fibroblasts (TAFs) or other stromal cells that act as critical coconspirators of tumor progression is very scarce. To address this gap of knowledge, we conducted genome-wide DNA methylation profiling on lung TAFs and paired control fibroblasts (CFs) from non-small cell lung cancer patients using the HumanMethylation450 microarray. We found widespread DNA hypomethylation concomitant with focal gain of DNA methylation in TAFs compared to CFs. The aberrant DNA methylation landscape of TAFs had a global impact on gene expression and a selective impact on the TGF-ß pathway. The latter included promoter hypermethylation-associated SMAD3 silencing, which was associated with hyperresponsiveness to exogenous TGF-ß1 in terms of contractility and extracellular matrix deposition. In turn, activation of CFs with exogenous TGF-ß1 partially mimicked the epigenetic alterations observed in TAFs, suggesting that TGF-ß1 may be necessary but not sufficient to elicit such alterations. Moreover, integrated pathway-enrichment analyses of the DNA methylation alterations revealed that a fraction of TAFs may be bone marrow-derived fibrocytes. Finally, survival analyses using DNA methylation and gene expression datasets identified aberrant DNA methylation on the EDARADD promoter sequence as a prognostic factor in non-small cell lung cancer patients. Our findings shed light on the unique origin and molecular alterations underlying the aberrant phenotype of lung TAFs, and identify a stromal biomarker with potential clinical relevance.

Synthetic benchmarks for machine olfaction: Classification, segmentation and sensor damage.

The design of the signal and data processing algorithms requires a validation stage and some data relevant for a validation procedure. While the practice to share public data sets and make use of them is a recent and still on-going activity in the community, the synthetic benchmarks presented here are an option for the researches, who need data for testing and comparing the algorithms under development. The collection of synthetic benchmark data sets were generated for classification, segmentation and sensor damage scenarios, each defined at 5 difficulty levels. The published data are related to the data simulation tool, which was used to create a virtual array of 1020 sensors with a default set of parameters [1].

Data set from gas sensor array under flow modulation.

Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point. The data presented here have been deposited to the web site of The University of California at Irvine (UCI) Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+under+flow+modulation). The code repository for reproducible analysis applied to the data is hosted at the GutHub web site (https://github.com/variani/pulmon). The data and code can be used upon citation of [1].

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation.

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices.

Intensity drift removal in LC/MS metabolomics by common variance compensation.

UNLABELLED: Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context. AVAILABILITY AND IMPLEMENTATION: The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit).

UNLABELLED: Current tools for liquid chromatography and mass spectrometry for metabolomic data cover a limited number of processing steps, whereas online tools are hard to use in a programmable fashion. This article introduces the Metabolite Automatic Identification Toolkit (MAIT) package, which makes it possible for users to perform metabolomic end-to-end liquid chromatography and mass spectrometry data analysis. MAIT is focused on improving the peak annotation stage and provides essential tools to validate statistical analysis results. MAIT generates output files with the statistical results, peak annotation and metabolite identification. AVAILABILITY AND IMPLEMENTATION: http://b2slab.upc.edu/software-and-downloads/metabolite-automatic-identification-toolkit/.

Effect of genetic regions on the correlation between single point mutation variability and morbidity.

Cross-species sequence comparisons have suggested that cross-species sequence variability is correlated with functionality. The goal of this study was to extend this observation at different genetic regions, focusing on the morbidity of Single Nucleotide Polymorphisms (SNPs). A set of deleterious SNPs was compared to a set of neutral SNPs. Both samples were stratified according to the location of the SNPs at different genetic regions. Deleterious SNPs were observed to be less variable across species than neutral SNPs, these differences being significant for missense mutations (p=1.1 × 10(-9)), SNPs in introns (p=6.4 × 10(-3)), and SNPs in unknown regions (p=8 × 10(-4)).